Machine Learning Researcher

G-Research; Greater London; On-site; GBP 60,000 - 80,000; Full time

Benefits offered by this job

Highly competitive compensation plus annual discretionary bonus

Lunch provided via Just Eat for Business

30 days’ annual leave

9% contributory pension scheme

Informal dress code

Comprehensive healthcare and life assurance

Job summary

G-Research in London is seeking a Machine Learning Researcher to develop models for forecasting financial time series. The ideal candidate will hold a post-graduate degree in machine learning or a related field, possess strong experience in advanced ML techniques, and have solid programming skills in Python and related libraries. The role provides access to vast data and computing resources, a competitive salary, 30 days annual leave, and comprehensive benefits including healthcare and a contributory pension scheme.

Qualifications

• Experience in developing novel machine learning algorithms.
• Experience in deep learning, reinforcement learning, non-convex optimisation, Bayesian non-parametrics, NLP, or approximate inference.
• Strong reasoning skills and mathematical ability.
• Strong programming skills and experience with Python and data science libraries.
• Publications at top conferences like NIPS, ICML, ICLR are highly desirable.

Responsibilities

• Develop models to forecast financial time series.
• Utilize vast amounts of structured and unstructured data.
• Apply machine learning methods from diverse areas.
• Develop and test ideas with real-world data.

Skills

Machine learning algorithms

Deep learning

Reinforcement learning

Non-convex optimisation

Bayesian non-parametrics

Natural Language Processing (NLP)

Approximate inference

Python

Scikit-Learn

SciPy

NumPy

Pandas

Jupyter Notebooks

Education

Post-graduate degree in machine learning or related discipline

Job description

We tackle the most complex problems in quantitative finance, by bringing scientific clarity to financial complexity.

From our London HQ, we unite world‑class researchers and engineers in an environment that values deep exploration and methodical execution - because the best ideas take time to evolve. Together we’re building a world‑class platform to amplify our teams’ most powerful ideas.

Join a research team where curiosity meets scale. You’ll investigate foundational questions, uncover market insights and push the boundaries of what's possible - all with the support of near‑limitless compute and world‑class peers.

Take the next step in your career.

The role

Our mission is to develop models to forecast financial time series. This is a challenging and highly competitive space so rather than deploy standard methods off the shelf you will likely need to extend classical methods or develop entirely new techniques. Our problems are well‑defined and success is highly measurable and has direct impact on the business.

You will have access to vast amounts of data (both structured and unstructured), large computing resources and a world class research platform. You will apply machine learning methods drawn from diverse areas such as neural networks, reinforcement learning, deep learning, non‑convex optimisation, Bayesian non‑parametrics, NLP and approximate inference. We read the latest publications in the field and discuss them within the firm’s vibrant research community. We attend the leading conferences worldwide (e.g. NIPS, ICML, ACL etc.).

This is a pure research role where you will be able to develop and test your ideas with real‑world data in an environment that resembles academia.

Who are we looking for?

The ideal candidate will at minimum have experience in the following areas:

• Either a post‑graduate degree in machine learning or a related discipline, or commercial experience developing novel machine learning algorithms. We will also consider exceptional candidates with a proven record of success in online data science competitions (e.g. Kaggle).
• Experience in one or more of deep learning, reinforcement learning, non‑convex optimisation, Bayesian non‑parametrics, NLP or approximate inference.
• Excellent reasoning skills and mathematical ability are crucial: off the shelf methods don’t always work on our data so you will need to understand how to develop your own models.
• Strong programming skills and experience working with Python, Scikit‑Learn, SciPy, NumPy, Pandas and Jupyter Notebooks is desirable. Experience with object oriented programming would be beneficial.
• Publications at top conferences such as NIPS, ICML, ICLR etc. is highly desirable.

Why should you apply?

• Highly competitive compensation plus annual discretionary bonus
• Lunch provided (via Just Eat for Business) and dedicated barista bar
• 30 days’ annual leave
• 9% contributory pension scheme
• Informal dress code and excellent work/life balance
• Comprehensive healthcare and life assurance